Climate and soil management factors control spatio-temporal variation of soil nutrients and soil organic matter in the farmland of Jiangxi Province in South China

Hu, Bifeng; Xie, Modian; Li, Hongyi; He, Rebin; Zhou, Yue; Jiang, Yefeng; Ji, Wenjun; Peng, Jie; Xia, Fang; Liang, Zhao; Deng, Wanming; Wang, Junjie; Shi, Zhou

doi:10.1007/s11368-023-03471-5

Cited by 13 publications

(2 citation statements)

References 82 publications

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“…2019) also found RF outperformed other methods for modelling SOM. This could attribute to several merits of RF, such as the ability to deal with high dimensionality, robust to outliers and non-linear data and low bias (Hu et al, 2023;Xiao et al, 2022). Thus, the RF model is widely used in the prediction of soil properties such as SOC (Xiao et al, 2022), pH (Chen et al, 2019), C:N ratio (van der Westhuizen et al, 2023), heavy metals (Hu, Xue, et al, 2020) and soil salinity (Zhou et al, 2022).…”

Section: Differences In Som Under Various Crop Rotation Systemsmentioning

confidence: 99%

Mapping soil organic matter and identifying potential controls in the farmland of Southern China: Integration of multi‐source data, machine learning and geostatistics

Hu,

Ni,

Xie

et al. 2023

Land Degrad Dev

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Soil organic matter (SOM) plays a critical role in terrestrial ecosystem functioning and is closely related to many global issues like soil fertility, soil health and climate regulation. Therefore, obtaining accurate information on the spatial distribution of SOM and its potential controlling factors is of global interest. However, this remains a great challenge since SOM is affected by numerous natural and anthropogenic factors and usually showed strong heterogeneity. In this study, we collected a total of 16,580 surface soil (0–20 cm) samples from the farmland throughout Jiangxi Province. And the Random Forest (RF), Cubist and gradient‐boosted models were compared and used to define the factor which is most associated with SOM. Then the ordinary kriging (OK) and machine learning‐ordinary co‐kriging (ML‐COK) were used to map SOM. We found that on average, 30.86 g kg−1 SOM was present in farmland soil of Jiangxi Province. Anthropogenic activities strongly affected SOM level, with five of the top 10 most important factors are anthropogenic related. The straw return amount was proved to have the largest importance (31.46%) for modelling SOM and a significant (p < 0.001) positive relationship between SOM content and the amount of straw returned to farmland was detected. Additionally, returning straw improved crop production. Soil derived from the Quaternary Subred Sand has the highest SOM content (37.82 g kg−1). Crop rotation also improved SOM content and the rice‐bean rotation system has the highest SOM content (34.27 g kg−1). With the best performance, the RF algorithm (R2 = 0.49, RMSE = 6.77 g kg−1) was selected to identify the primary control of SOM and integrated with COK, which we termed as ML‐COK, to map the SOM in the farmland of Jiangxi Province. ML‐COK outperformed OK method for mapping the SOM in farmland of Jiangxi Province with R2 of 0.351 and Lin's concordance correlation coefficient of 0.549. Farmland distributed in the central part of the province had high SOM content. In contrast, farmland in the north, south and east parts had relatively low SOM. Our study offers new insight for mapping soil properties, identifying potential factors driving variation in SOM, and also provides valuable information for making more reasonable and environmentally friendly farmland management measures.

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Section: Differences In Som Under Various Crop Rotation Systemsmentioning

confidence: 99%

Mapping soil organic matter and identifying potential controls in the farmland of Southern China: Integration of multi‐source data, machine learning and geostatistics

Hu,

Ni,

Xie

et al. 2023

Land Degrad Dev

Self Cite

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“…Cropland, being one of the most significant terrestrial vegetation types, is essential for the sustainable development of national economies [16][17][18][19]. In addition to producing grains, vegetables, and fiber for humans, cropland also performs important roles in ecosystem services such as soil protection, carbon sequestration, and gas management [20][21][22]. Cropland vegetation is much more complex than other types of vegetation changes due to farmers and government policies [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Diverse Response of Cropland Vegetation to Climatic Factors and Irrigation across China

Sun,

Zhong,

Ding

et al. 2024

Agronomy

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Owing to limited research on the interactions between cropland vegetation and climate and irrigation, this study used the normalized difference vegetation index (NDVI) as a cropland vegetation indicator to describe vegetation dynamics. Potential evapotranspiration (PET) was calculated using the Penman–Monteith equation. A partial correlation analysis and a Pearson correlation coefficient were used to determine the spatial response mechanisms of cropland vegetation to different climatic factors and irrigation in China for the period 1985–2015. The results show that different climatic factors (precipitation, PET, and water deficits) display positive correlations with cropland vegetation in China. A stronger correlation was observed between cropland vegetation and meteorological factors in northern China compared to the southern parts; the response time of NDVI values of croplands to precipitation was observed to be short-term (1 to 3 months) and long-term (3 to 6 months) in northern and southern regions, respectively. In contrast, the response time of NDVI values of croplands to PET displayed a complex spatial heterogeneity. Most of the cropland vegetation and the areas with the highest potential crop yields were located in the eastern part of China; these areas also require higher levels of irrigation, which benefits the potential crop yields. This study can provide a better understanding of the agricultural ecosystems and formulate strategies for food security.

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Identifying conservation priority zones and their driving factors regarding regional ecosystem services

et al. 2023

Environ Dev Sustain

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Climate and soil management factors control spatio-temporal variation of soil nutrients and soil organic matter in the farmland of Jiangxi Province in South China

Cited by 13 publications

References 82 publications

Mapping soil organic matter and identifying potential controls in the farmland of Southern China: Integration of multi‐source data, machine learning and geostatistics

Mapping soil organic matter and identifying potential controls in the farmland of Southern China: Integration of multi‐source data, machine learning and geostatistics

Exploring the Diverse Response of Cropland Vegetation to Climatic Factors and Irrigation across China

Identifying conservation priority zones and their driving factors regarding regional ecosystem services

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